Merge branch 'gramhagen/lgbm_scenario' of github.com:Microsoft/Recommenders into gramhagen/lgbm_scenario

README.md

@@ -53,7 +53,7 @@ The table below lists recommender algorithms available in the repository at the
 | [FastAI Embedding Dot Bias (FAST)](notebooks/00_quick_start/fastai_movielens.ipynb)  |  Python CPU / Python GPU | Collaborative Filtering | General purpose algorithm with embeddings and biases for users and items |
 | [Alternating Least Squares (ALS)](notebooks/00_quick_start/als_movielens.ipynb) | PySpark | Collaborative Filtering | Matrix factorization algorithm for explicit or implicit feedback in large datasets, optimized by Spark MLLib for scalability and distributed computing capability | 
 | [Vowpal Wabbit Family (VW)<sup>*</sup>](notebooks/02_model/vowpal_wabbit_deep_dive.ipynb) | Python CPU (train online) | Collaborative, Content-Based Filtering | Fast online learning algorithms, great for scenarios where user features / context are constantly changing |
-| [LightGBM/Gradient Boosting Tree<sup>*</sup>](notebooks/00_quick_start/lightgbm_tinycriteo.ipynb) | Python CPU | Content-Based Filtering | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems |
+| [LightGBM/Gradient Boosting Tree<sup>*</sup>](notebooks/00_quick_start/lightgbm_tinycriteo.ipynb) | Python CPU / PySpark | Content-Based Filtering | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems |
 | [Deep Knowledge-Aware Network (DKN)<sup>*</sup>](notebooks/00_quick_start/dkn_synthetic.ipynb) |  Python CPU / Python GPU | Content-Based Filtering | Deep learning algorithm incorporating a knowledge graph and article embeddings to provide powerful news or article recommendations | 
 | [Extreme Deep Factorization Machine (xDeepFM)<sup>*</sup>](notebooks/00_quick_start/xdeepfm_synthetic.ipynb) | Python CPU / Python GPU | Hybrid | Deep learning based algorithm for implicit and explicit feedback with user/item features | 
 | [Wide and Deep](notebooks/00_quick_start/wide_deep_movielens.ipynb) | Python CPU / Python GPU | Hybrid | Deep learning algorithm that can memorize feature interactions and generalize user features |

SETUP.md

@@ -94,6 +94,7 @@ To set these variables every time the environment is activated, we can follow th
 #!/bin/sh
 export PYSPARK_PYTHON=/anaconda/envs/reco_pyspark/bin/python
 export PYSPARK_DRIVER_PYTHON=/anaconda/envs/reco_pyspark/bin/python
+unset SPARK_HOME
 ```
 
 This will export the variables every time we do `conda activate reco_pyspark`. To unset these variables when we deactivate the environment, we create the file `/anaconda/envs/reco_pyspark/etc/conda/deactivate.d/env_vars.sh` and add:

notebooks/02_model/README.md

@@ -7,6 +7,7 @@ In this directory, notebooks are provided to give a deep dive into training mode
 | Notebook | Environment | Description |
 | --- | --- | --- |
 | [als_deep_dive](als_deep_dive.ipynb) | PySpark | Deep dive on the ALS algorithm and implementation.
+| [mmlspark_lightgbm_criteo](mmlspark_lightgbm_criteo.ipynb) | PySpark | LightGBM gradient boosting tree algorithm implementation in MML Spark with Criteo dataset.
 | [baseline_deep_dive](baseline_deep_dive.ipynb) | --- | Deep dive on baseline performance estimation.
 | [ncf_deep_dive](ncf_deep_dive.ipynb) | Python CPU, GPU | Deep dive on a NCF algorithm and implementation.
 | [rbm_deep_dive](rbm_deep_dive.ipynb)| Python CPU, GPU | Deep dive on the rbm algorithm and its implementation.